Incarceration Rates of Persons With First-Admission Psychosis

This prospective study examined jail stay in a cohort of persons with schizophrenia and other psychotic disorders who experienced their first hospital admission and who were participating in the Suffolk County Mental Health Project. Demographic and clinical risk factors for jail placement were investigated over a four-year period after hospital discharge. The sample included 538 first-admission respondents diagnosed as meeting DSM-IV criteria for having schizophrenia, psychotic mood disorder, or other psychotic disorders. Initial interviews occurred in the hospital; face-to-face follow-ups occurred at the six-, 24-, and 48-month points, and telephone contact was maintained every three to six months. Multivariate logistic regression analysis was used to examine the demographic and clinical risk factors. Forty-seven respondents (9%) were incarcerated over the follow-up period. Among them, 20 were incarcerated multiple times. The prevalence, incidence, reasons for incarceration, and time served did not vary significantly by diagnosis. The most significant predictors of jail stay and time to incarceration during the follow-up were being male or black and having been incarcerated before admission. Predictive effects of other risk factors (for example, symptom severity or substance abuse) were smaller or statistically insignificant. The results suggest a need for mental health care professionals to routinely evaluate, document, and collaboratively address incarceration history, especially when working with black males, in an effort to avert future incarceration.

Characteristics of Inpatients With a History of Recurrent Psychiatric Hospitalizations: A Matched-Control Study

This study examined the reliability and convergent, discriminant, and predictive validity of the Mental Illness Research, Education, and Clinical Center (MIRECC) version of the Global Assessment of Functioning (GAF) scale. The MIRECC GAF measures occupational functioning, social functioning, and symptom severity on three subscales. MIRECC GAF ratings were obtained for 398 individuals with schizophrenia or schizoaffective disorder who were receiving treatment at three Veterans Affairs mental health clinics. Assessments were completed by using the Positive and Negative Syndrome Scale and the Quality of Life Interview at baseline and nine months later. All three MIRECC GAF subscales exhibited very high levels of reliability. The occupational and symptom subscales showed good convergent and discriminant validity. The social subscale was related to measures of social functioning and, to a greater degree, symptom severity. The occupational and social subscales significantly predicted their respective domains at the nine-month follow-up. The symptom subscale predicted negative symptoms at follow-up; however, it did not predict positive symptoms or cognitive disorientation. Instead, the social subscale was predictive of cognitive disorientation at follow-up. When the standard GAF was routinely administered by clinicians, scores demonstrated little validity. The three MIRECC GAF subscales can be scored reliably, and they have good concurrent and predictive validity. Further work is needed on brief measures of patient functioning, especially measures of social functioning.

Factors Predicting the Presence of Suicidal Ideation in Patients Presenting to the Emergency Room

Kraepelin considered declining course a hallmark of schizophrenia, but others have suggested that outcomes usually stabilize or improve after treatment initiation. The authors investigated this question in an epidemiologically defined cohort with psychotic disorders followed for 20 years after first hospitalization. The Suffolk County Mental Health Project recruited first-admission patients with psychosis from all inpatient units of Suffolk County, New York (response rate, 72%). Participants were assessed in person six times over two decades; 373 completed the 20-year follow-up (68% of survivors); 175 had schizophrenia/schizoaffective disorder. Global Assessment of Functioning (GAF), psychotic symptoms, and mood symptoms were rated at each assessment. Month 6, when nearly all participants were discharged from the index hospitalization, was used as a reference. In the schizophrenia group, mean GAF scores declined from 49 at month 6 to 36 at year 20. Negative and positive symptoms also worsened (Cohen's d values, 0.45-0.73). Among participants without schizophrenia, GAF scores were higher initially (a mean of approximately 64) but declined by 9 points over the follow-up period. Worsening began between years 5 and 8. Neither aging nor changes in antipsychotic treatment accounted for the declines. In all disorders, depression improved and manic symptoms remained low across the 20 years. The authors found substantial symptom burden across disorders that increased with time and ultimately may undo initial treatment gains. Previous studies have suggested that better health care delivery models may preempt this decline. In the United States, these care needs are often not met, and addressing them is an urgent priority.

Abstract. A small number of studies have investigated predictive factors in relation to the Global Assessment of Functioning (GAF) scale. This study aimed to explore the influence of clinical and socio-demographic factors in a psychiatric inpatient setting in relation to treatment outcome measured by the GAF. The studied psychiatric inpatient sample consisted of 816 episodes of care, with GAF ratings made at both admission and discharge. Multiple linear regressions were performed to analyze what variables predicted GAF scores at admission and at discharge. Significant predictors of GAF scores at admission were age, schizophrenia, other psychotic disorders, and no registered diagnosis. GAF scores at admission, patients’ diagnoses, and ward affiliation were able to significantly predict GAF at discharge. Specialized wards did not necessarily deliver the best treatment results in spite of their diagnostic specialization. This study provides support to the construct validity of the GAF when used as a measure of outcome.

To estimate how much information conveyed by self-reported family history of heart disease (FHHD) is already explained by clinical and genetic risk factors. Cross-sectional analysis of UK Biobank participants without pre-existing coronary artery disease using a multivariable model with self-reported FHHD as the outcome. Clinical (diabetes, hypertension, smoking, apolipoprotein B-to-apolipoprotein AI ratio, waist-to-hip ratio, high sensitivity C-reactive protein, lipoprotein(a), triglycerides) and genetic risk factors (polygenic risk score for coronary artery disease [PRSCAD], heterozygous familial hypercholesterolemia [HeFH]) were exposures. Models were adjusted for age, sex, and cholesterol-lowering medication use. Multiple logistic regression models were fitted to associate FHHD with risk factors, with continuous variables treated as quintiles. Population attributable risks (PAR) were subsequently calculated from the resultant odds ratios. Among 166 714 individuals, 72 052 (43.2%) participants reported an FHHD. In a multivariable model, genetic risk factors PRSCAD (OR 1.30, CI 1.27-1.33) and HeFH (OR 1.31, 1.11-1.54) were most strongly associated with FHHD. Clinical risk factors followed: hypertension (OR 1.18, CI 1.15-1.21), lipoprotein(a) (OR 1.17, CI 1.14-1.20), apolipoprotein B-to-apolipoprotein AI ratio (OR 1.13, 95% CI 1.10-1.16), and triglycerides (OR 1.07, CI 1.04-1.10). For the PAR analyses: 21.9% (CI 18.19-25.63) of the risk of reporting an FHHD is attributed to clinical factors, 22.2% (CI% 20.44-23.88) is attributed to genetic factors, and 36.0% (CI 33.31-38.68) is attributed to genetic and clinical factors combined. A combined model of clinical and genetic risk factors explains only 36% of the likelihood of FHHD, implying additional value in the family history.

TABLE OF CONTENTSPreamble 686Definition of the Problem 688Purpose of These Guidelines 688Methodology and Evidence 689General Approach to the Patient 692History 693Physical Examination and Routine Laboratory Tests 693Multivariable Indices to Predict Preoperative Cardiac Morbidity 694Clinical Assessme

Polygenic risk scores (PRSs) have proven to be as strong as or stronger than established clinical risk factors for many cardiovascular phenotypes. Whether this is true for aortic stenosis remains unknown. To develop a novel aortic stenosis PRS and compare its aortic stenosis risk estimation to established clinical risk factors. This was a longitudinal cohort study using data from the Million Veteran Program (MVP; 2011-2020), UK Biobank (2006-2010), and 6 Thrombolysis in Myocardial Infarction (TIMI) trials, including DECLARE-TIMI 58 (2013-2018), FOURIER (TIMI 59; 2013-2017), PEGASUS-TIMI 54 (2010-2014), SAVOR-TIMI 53 (2010-2013), SOLID-TIMI 52 (2009-2014), and ENGAGE AF-TIMI 48 (2008-2013), which were a mix of population-based and randomized clinical trials. Individuals from UK Biobank and the MVP meeting a previously validated case/control definition for aortic stenosis were included. All individuals from TIMI trials were included unless they had a documented preexisting aortic valve replacement. Analysis took place from January 2022 to December 2023. PRS for aortic stenosis (developed using data from MVP and validated in UK Biobank) and other previously validated cardiovascular PRSs, defined either as a continuous variable or as low (bottom 20%), intermediate, and high (top 20%), and clinical risk factors. Aortic stenosis (defined using International Classification of Diseases or Current Procedural Terminology codes in UK Biobank and MVP or safety event data in the TIMI trials). The median (IQR) age in MVP was 67 (57-73) years, and 135 140 of 147 104 participants (92%) were male. The median (IQR) age in the TIMI trials was 66 (54-78) years, and 45 524 of 59 866 participants (71%) were male. The best aortic stenosis PRS incorporated 5 170 041 single-nucleotide variants and was associated with aortic stenosis in both the MVP testing sample (odds ratio, 1.41; 95% CI, 1.37-1.45 per 1 SD PRS; P = 4.6 × 10-116) and TIMI trials (hazard ratio, 1.44; 95% CI, 1.27-1.62 per 1 SD PRS; P = 3.2 × 10-9). Among genetic and clinical risk factors, the aortic stenosis PRS performed comparably to most risk factors besides age, and within a given age range, the combination of clinical and genetic risk factors was additive, providing a 3- to 4-fold increased gradient of risk of aortic stenosis. However, the addition of the aortic stenosis PRS to a model including clinical risk factors only improved risk discrimination of aortic stenosis by 0.01 to 0.02 (C index in MVP: 0.78 with clinical risk factors, 0.79 with risk factors and aortic stenosis PRS; C index in TIMI: 0.71 with clinical risk factors, 0.73 with risk factors and aortic stenosis PRS). This study developed and validated 1 of the first aortic stenosis PRSs. While aortic stenosis genetic risk was independent from clinical risk factors and performed comparably to all other risk factors besides age, genetic risk resulted in only a small improvement in overall aortic stenosis risk discrimination beyond age and clinical risk factors. This work sets the stage for further development of an aortic stenosis PRS.

Background: In recent years, the prevalence of thyroid nodules (TNs) has been increasing, but the relationship between metabolic abnormalities and the incidence of TNs is not well defined, and there is scant data evaluating this relationship stratified by gender. This study aims to analyze the prevalence of TNs and possible risk factors for TNs across gender lines and various metabolic states in Beijing, China. Patients and Methods: A total of 6001 subjects who underwent thyroid ultrasounds as part of a routine medical checkup at Luhe Hospital between 2017 and 2018 were enrolled in this study. Multivariate adjustment logic was used to analyze possible demographic and clinical risk factors of TN stratified by gender. Results: The prevalence of TNs was 44.1%, of which 45.9% were female and 40% were male. In general, the prevalence of TNs increased in parallel with advancing age. These findings were even starker among females, with TN prevalences of 37.5%, 46.5%, 52.9%, and 54.1%, among participants in <55-, 55–65-, 65–75-, and >75-year-old age groups, respectively. The prevalence of TNs was significantly higher among patients with obesity (46.8% vs. 43%, P = 0.008), central obesity (45% vs. 40.4%, P = 0.005), hypertension (47.1% vs. 42.4%, P < 0.001), metabolic syndrome (MetS) (46.1% vs. 41%, P < 0.001), and low TSH levels (46.5% vs. 37.1%, P < 0.001). MetS and obesity were independent risk factors for the prevalence of TNs (odds ratio [OR] = 1.167, [1.002–1.277] and (OR = 0.038, [1.01–1.396]), respectively). TSH had a protective effect on the prevalence of TNs (OR = 0.664, [0.585–0.75]). Conclusions: The present study supports the existing research that contends a strong correlation between older age, MetS, and other clinical risk factors and the prevalence of TNs. This relationship only persisted among women when stratified by gender. These results set the precedent for further research on how gender influences the incidence of TNs, particularly in the setting of other clinical and demographic risk factors.

BackgroundBlack patients tend to develop coronary artery disease at a younger age than other groups. Previous data on racial disparities in outcomes of myocardial infarction (MI) have been inconsistent and limited to older populations. Our objective was to investigate racial differences in the outcome of MI among young and middle‐aged patients and the role played by socioeconomic, psychosocial, and clinical differences.Methods and ResultsWe studied 313 participants (65% non‐Hispanic Black) <61 years old hospitalized for confirmed type 1 MI at Emory‐affiliated hospitals and followed them for 5 years. We used Cox proportional‐hazard models to estimate the association of race with a composite end point of recurrent MI, stroke, heart failure, or cardiovascular death after adjusting for demographic, socioeceonomic status, psychological, and clinical risk factors. The mean age was 50 years, and 50% were women. Compared with non‐Black patients, Black patients had lower socioeconomic status and more clinical and psychosocial risk factors but less angiographic coronary artery disease. The 5‐year incidence of cardiovascular events was higher in Black (35%) compared to non‐Black patients (19%): hazard ratio (HR) 2.1, 95% CI, 1.3 to 3.6. Adjustment for socioeconomic status weakened the association (HR 1.3, 95% CI, 0.8–2.4) more than adjustment for clinical and psychological risk factors. A lower income explained 46% of the race‐related disparity in outcome.ConclusionsAmong young and middle‐aged adult survivors of an MI, Black patients have a 2‐fold higher risk of adverse outcomes, which is largely driven by upstream socioeconomic factors rather than downstream psychological and clinical risk factors.

The Health Care for Reentry Veterans (HCRV) program provides Veterans Health Administration outreach services to veterans incarcerated in state and federal prisons. This study used HCRV data to compare risk of incarceration of veterans of Operations Enduring Freedom (OEF), Iraqi Freedom (OIF), and New Dawn (OND) and other veterans and to identify sociodemographic and clinical characteristics of incarcerated veterans of OEF/OIF/OND. Administrative national data were analyzed for 30,968 incarcerated veterans, including 1,201 OEF/OIF/OND veterans, contacted from October 2007 to April 2011. Odds ratios were calculated comparing the risk of incarceration among OEF/OIF/OND and other veterans in the HCRV sample and in a weighted sample of nonincarcerated veterans from the 2010 National Survey of Veterans. Stepwise logistic regressions of HCRV data examined characteristics of incarcerated veterans independently associated with OEF/OIF/OND service. Regardless of ethnicity or age, OEF/OIF/OND veterans were less than half as likely as other veterans to be incarcerated and constituted only 3.9% of the incarcerated veterans. Compared with other incarcerated veterans, OEF/OIF/OND veterans were younger, were more likely to be married, were more likely to report combat exposure, expected a shorter incarceration, were 26% less likely to have a diagnosis of drug abuse or dependence, and were three times more likely to have combat-related posttraumatic stress disorder (PTSD). OEF/OIF/OND veterans appeared to be at lower risk of incarceration than veterans of other service eras, but those who were incarcerated had higher rates of PTSD. Efforts to link these veterans to mental health services upon their release are warranted.

Juvenile idiopathic arthritis (JIA)-associated uveitis (JIAU) is a serious JIA comorbidity that can result in vision impairment. This study aimed to identify genetic risk factors within the major histocompatibility complex for JIAU and evaluate their contribution for improving risk classification when combined with clinical risk factors. Data on single nucleotide polymorphisms, amino acids, and classical HLA alleles were available for 2,497 patients with JIA without uveitis and 579 patients with JIAU (female 2,060, male 1,015). Analysis was restricted to patients with inferred European ancestry. Forward conditional logistic regression identified genetic markers exceeding a Bonferroni-corrected significance (6 × 10-6). Multivariable logistic regression estimated the effects of clinical and genetic risk factors, and a likelihood ratio test calculated the improvement in model fit when adding genetic factors. Uveitis risk classification performance of a model integrating genetic and clinical risk factors was estimated using area under the receiver operator characteristic curve and compared with a model of clinical risk factors alone. Three genetic risk factors were identified, mapping to HLA-DRB1, HLA-DPB1, and HLA-A. These markers were statistically independent from clinical risk factors and significantly improved the fit of a model when included with clinical risk factors (P = 3.3 × 10-23). The addition of genetic markers improved the classification of JIAU compared with a model of clinical risk factors alone (area under the curve 0.75 vs 0.71). Integration of a genetic and clinical risk prediction model outperforms a model based solely on clinical risk factors. Future JIAU risk prediction models should include genetic risk factors.

Research Type: Level 3 - Retrospective cohort study, Case-control study, Meta-analysis of Level 3 studies Introduction/Purpose: Ankle arthroscopy is a widely performed procedure for diagnosing and treating ankle pathologies. While generally safe, surgical site infections (SSIs) remain a concern, with reported incidences ranging from 0.13% to 3.08%. Although relatively uncommon, SSIs can delay rehabilitation, increase healthcare costs, and lead to severe complications like septic arthritis. Identifying predictors of infection is crucial to improving surgical outcomes. Previous studies suggest that diabetes mellitus (DM) and high body mass index (BMI) may be associated with increased infection risk, but data remain limited. This 8-year retrospective study aimed to determine the incidence of postoperative infections in ankle arthroscopy and identify significant predictors of SSI, using a multivariate analysis of demographic and clinical risk factors. Methods: This retrospective cohort study included 321 patients who underwent ankle arthroscopy between 2014 and 2022. Patients were excluded if they had pre-existing infections, open fractures, or incomplete medical records. Demographic and clinical data, including age, sex, BMI, smoking status, diabetes mellitus (DM), hypertension (SAH), and ASA score, were collected. The primary outcome was SSI incidence, with infections classified as superficial (managed conservatively) or deep (requiring surgical debridement or implant removal). Multivariate logistic regression was performed to identify independent predictors of infection, controlling for potential confounders. Statistical significance was set at p &lt; 0.05. This study provides one of the largest long-term analyses on infection risk factors in ankle arthroscopy, offering critical insights into patient selection and perioperative management strategies. Results: The overall SSI incidence was 2.5% (8/321 patients), with 6 superficial and 2 deep infections requiring further surgery. Diabetes mellitus (DM) and high BMI emerged as independent predictors of infection. Patients with BMI ≥40 kg/m² had a 50% infection rate (p = 0.008), while DM patients had an 11.8% infection rate (p = 0.013). No significant associations were found with hypertension (p = 0.862), smoking (p = 0.254), ASA score (p = 0.616), or sex (p = 0.075). The most common indications for arthroscopy were synovectomy, impingement, and loose body removal (48.3%), followed by osteochondral defect treatment (16.8%) and ligament repair (12.8%). These findings suggest that BMI and DM should be considered in preoperative risk stratification. Conclusion: This 8-year study confirms that BMI and diabetes mellitus are independent predictors of postoperative infection in ankle arthroscopy, while other demographic factors, including hypertension, smoking, and ASA score, showed no significant correlation. Although the overall infection rate was low (2.5%), deep infections requiring surgical intervention reinforce the importance of early recognition and risk-based perioperative management. These findings support the implementation of targeted infection prevention strategies, particularly in patients with high BMI and diabetes, to optimize surgical outcomes and minimize complications. Preoperative screening and tailored postoperative care are essential to reducing infection risk in this patient population. Fisher’s Exact Test Results for Risk Factors Associated with Infection in Ankle Arthroscopy This table presents the association between clinical and demographic risk factors and surgical site infections (SSIs) following ankle arthroscopy. Diabetes mellitus (p = 0.013) and BMI (p = 0.008) were significantly associated with infection, indicating their role as independent risk factors. Other variables, including hypertension, smoking, gender, ASA score, and type of surgical procedure, did not show statistically significant associations (p &gt; 0.05).

Antidepressants (ADs) play a valuable role in treating the depressive episodes of bipolar disorder. However, 14% of these individuals taking ADs experience AD-associated mania (AAM) within a few weeks of starting treatment. Numerous studies have suggested potential clinical and genetic risk factors. We aimed to conduct a comprehensive systematic review and meta-analysis that integrates the past literature with the recent studies and identifies important predictors for AAM. The review was limited to experimentally designed studies that contain the relevant search terms in PubMed and PsychInfo. After removing studies that were in discordance with our criteria, the review included 24 reports examining clinical risk factors and 10 investigating genetic risk factors. Our meta-analysis was conducted on 5 clinical risk factors, each of which had at least 4 articles with extractable data. The only clinical factors in the literature that have been shown to be more indicative of AAM risk are AD monotherapy and tricyclic ADs. Among genetic factors, the serotonin transporter gene polymorphism may play a minor role in AAM. Our meta-analysis provided support for the number of prior depressive episodes. Prevention of AAM may be served by early detection of recurrent depression episodes. Further large-scale longitudinal studies are required to determine the underpinnings of AAM.